Data Specialization Course
Videos: search Brian Caffo on YouTube
Code: https://github.com/DataScienceSpecialization/courses and
Notes: http://datasciencespecialization.github.io/
ISLR Videos and Notes https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
library(UsingR)
data(diamonds)
library(ggplot2)
fit <- lm(price ~ carat, data = diamond)
coef(fit)
(Intercept) carat
-259.6259 3721.0249
fit2 <- lm(price ~ I(carat - mean(carat)), data = diamond)
coef(fit2)
## (Intercept) I(carat - mean(carat))
## 500.0833 3721.0249
newx <- c(0.16, 0.27, 0.34)
predict(fit, newdata = data.frame(carat = newx))
## 1 2 3
## 335.7381 745.0508 1005.5225
data(diamond)
plot(diamond$carat, diamond$price,
xlab = "Mass (carats)",
ylab = "Price (SIN $)",
bg = "lightblue",
col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(price ~ carat, data = diamond), lwd = 2)
points(diamond$carat, predict(fit), pch = 19, col = "red")
g <- ggplot(diamond, aes(x=carat, y=price))
g <- g + xlab("Mass (carats)")
g <- g + ylab("Price (dollars)")
g <- g + geom_point(size = 6, colour = "black", alpha = 0.2)
g <- g + geom_point(size = 5, colour = "blue", alpha = 0.2)
g <- g + geom_smooth(method="lm", colour="black")
g
https://plot.ly/ggplot2/geom_abline/
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:Hmisc':
##
## subplot
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:MASS':
##
## select
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
p <- ggplotly(g)
p
e <- resid(fit)
#or
fit$residuals
## 1 2 3 4 5 6
## -17.9483176 -7.7380691 -22.9483176 -85.1585661 -28.6303057 6.2619309
## 7 8 9 10 11 12
## 23.4721795 37.6311854 -38.7893116 24.4721795 51.8414339 40.7389488
## 13 14 15 16 17 18
## 0.2619309 13.4209369 -1.2098087 40.5287002 36.1029250 -44.8405542
## 19 20 21 22 23 24
## 79.3696943 -25.0508027 57.8414339 9.2619309 -20.9483176 -3.7380691
## 25 26 27 28 29 30
## -19.9483176 27.8414339 -54.9483176 8.8414339 -26.9483176 16.4721795
## 31 32 33 34 35 36
## -22.9483176 -13.1020453 -12.1020453 -0.5278205 3.2619309 2.2619309
## 37 38 39 40 41 42
## -1.2098087 -43.2098087 -27.9483176 -23.3122938 -15.6303057 43.2672091
## 43 44 45 46 47 48
## 32.8414339 7.3696943 4.3696943 -11.5278205 -14.8405542 17.4721795
#